Physical Sciences and Mathematics Commons^™

Performance Of The Parallelized Monte-Carlo Tree Search Approach For Dots And Boxes, Pranay Agrawal, Uta Ziegler

Posters-at-the-Capitol

The Monte-Carlo tree search (MCTS) is a method designed to solve difficult learning problems. MCTS performs random simulations from the current situation and stores the results in order to distinguish decisions based on their past success. MCTS will then select the best decision and finally repeat the process. Parallelizing the MCTS means to divide the learning process among independent learners. Then, after a fixed number of simulations, the data is shared and combined. Past research has shown that this approach is faster than non-parallelized approaches. Therefore, we anticipated that the time reduced from dividing the learning outweighs the potential costs …

End-To-End Deep Learning Systems For Scene Understanding, Path Planning And Navigation In Fire Fighter Teams, Manish Bhattarai

Shared Knowledge Conference

Firefighting is a dynamic activity with many operations occurring simultaneously. Maintaining situational awareness, defined as knowledge of current conditions and activities at the scene, are critical to accurate decision making. Firefighters often carry various sensors in their personal equipment, namely thermal cameras, gas sensors, and microphones. Improved data processing techniques can mine this data more effectively and be used to improve situational awareness at all times thereby improving real-time decision making and minimizing errors in judgment induced by environmental conditions and anxiety levels. This objective of this research employs state of the art Machine Learning (ML) techniques to create an …

Applied Cognitive Computing And Artificial Intelligence: How Machines Learn To “Read” The Law, Vern R. Walker

Legal Tech Boot Camp

No abstract provided.

Deep Machine Learning For Mechanical Performance And Failure Prediction, Elijah Reber, Nickolas D. Winovich, Guang Lin

The Summer Undergraduate Research Fellowship (SURF) Symposium

Deep learning has provided opportunities for advancement in many fields. One such opportunity is being able to accurately predict real world events. Ensuring proper motor function and being able to predict energy output is a valuable asset for owners of wind turbines. In this paper, we look at how effective a deep neural network is at predicting the failure or energy output of a wind turbine. A data set was obtained that contained sensor data from 17 wind turbines over 13 months, measuring numerous variables, such as spindle speed and blade position and whether or not the wind turbine experienced …

Investigating Dataset Distinctiveness, Andrew Ulmer, Kent W. Gauen, Yung-Hsiang Lu, Zohar R. Kapach, Daniel P. Merrick

The Summer Undergraduate Research Fellowship (SURF) Symposium

Just as a human might struggle to interpret another human’s handwriting, a computer vision program might fail when asked to perform one task in two different domains. To be more specific, visualize a self-driving car as a human driver who had only ever driven on clear, sunny days, during daylight hours. This driver – the self-driving car – would inevitably face a significant challenge when asked to drive when it is violently raining or foggy during the night, putting the safety of its passengers in danger. An extensive understanding of the data we use to teach computer vision models – …

Deep Neural Network Architectures For Modulation Classification Using Principal Component Analysis, Sharan Ramjee, Shengtai Ju, Diyu Yang, Aly El Gamal

The Summer Undergraduate Research Fellowship (SURF) Symposium

In this work, we investigate the application of Principal Component Analysis to the task of wireless signal modulation recognition using deep neural network architectures. Sampling signals at the Nyquist rate, which is often very high, requires a large amount of energy and space to collect and store the samples. Moreover, the time taken to train neural networks for the task of modulation classification is large due to the large number of samples. These problems can be drastically reduced using Principal Component Analysis, which is a technique that allows us to reduce the dimensionality or number of features of the samples …

Using Eeg-Validated Music Emotion Recognition Techniques To Classify Multi-Genre Popular Music For Therapeutic Purposes, Dejoy Shastikk Kumaran

The International Student Science Fair 2018

Music is observed to possess significant beneficial effects to human mental health, especially for patients undergoing therapy and older adults. Prior research focusing on machine recognition of the emotion music induces by classifying low-level music features has utilized subjective annotation to label data for classification. We validate this approach by using an electroencephalography-based approach to cross-check the predictions of music emotion made with the predictions from low-level music feature data as well as collected subjective annotation data. Collecting 8-channel EEG data from 10 participants listening to segments of 40 songs from 5 different genres, we obtain a subject-independent classification accuracy …

Using Eeg-Validated Music Emotion Recognition Techniques To Classify Multi-Genre Popular Music For Therapeutic Purposes, Dejoy Shastikk Kumaran

The International Student Science Fair 2018

Music is observed to possess significant beneficial effects to human mental health, especially for patients undergoing therapy and older adults. Prior research focusing on machine recognition of the emotion music induces by classifying low-level music features has utilized subjective annotation to label data for classification. We validate this approach by using an electroencephalography-based approach to cross-check the predictions of music emotion made with the predictions from low-level music feature data as well as collected subjective annotation data. Collecting 8-channel EEG data from 10 participants listening to segments of 40 songs from 5 different genres, we obtain a subject-independent classification accuracy …

Augustana Invitational Robotics Challenge 2018, Forrest Stonedahl

Celebration of Learning

We will be hosting the 3rd Annual Augustana Invitational Robotics Challenge. This event will involve student teams from Augustana and potentially several other schools in the region bringing forth the robots that they have designed, built, and programmed, to compete against one another. This year's challenge task involves the careful relocation of soda pop cans.

Self-Coaching With Ai: Developing Thinking Skills, Thinking Dispositions, And Well-Being, Olivier Malafronte, Isla Reddin, Roy Van Den Brink-Budgen

ICOT 18 - International Conference on Thinking - Cultivating Mindsets for Global Citizens

Being motivated by the need to address the challenges of our Volatile Uncertain Complex Ambiguous world, we strive to create tools to improve people’s lives and help them become more resilient, resourceful, self-confidant, and successful.

In a digital world, we must understand how to efficiently connect to digital systems. Connecting “with AI” doesn’t mean spending more time on digital devices, but spending time in a deliberate way with purpose and intentional learning outcomes.

As a society, we want to see graduates with emotional intelligence and reflective skills in order to address global economic and social issues. As for jobs …

Forecasting Smart Meter Energy Usage Using Distributed Systems And Machine Learning, Feiran Ji, Chris Dong, Lingzhi Du, Zizhen Song, Yuedi Zheng, Paul Intrevado

Creative Activity and Research Day - CARD

In this research, we explore the technical and computational merits of a machine learning algorithm on a large data set, employing distributed systems. Using 167 million(10 GB) energy consumption observations collected by smart meters from residential consumers in London, England, we predict future residential energy consumption using a Random Forest machine learning algorithm. Distributed systems such as AWS S3 and EMR, MongoDB and Apache Spark are used. Computational times and predictive accuracy are evaluated. We conclude that there are significant computational advantages to using distributed systems when applying machine learning algorithms on large-scale data. We also observe that distributed systems …

Cerebral Necrosis Research With Machine Learning Techniques, Sangyu Shen

Creative Activity and Research Day - CARD

Cerebral necrosis after radiotherapy for patients with brain metastases is being recognized as a problem more common than previously estimated. To better understand the onset of necrosis and reduce its occurrence, we studied the relationships between features of patients and necrosis onset with machine learning techniques.

An Optimization Approach To Automate The Generation Of Radiotherapy Treatment Plans, Qian Li

Creative Activity and Research Day - CARD

The main goal of radiotherapy is to deliver a specified dose of radiation directly to the tumor while minimizing radiation damage to healthy tissues. Currently, the treatment plan is being developed by professional planners using a commercial treatment planning system. In this treatment planning system, the planner modifies the objectives and weights of the objectives until an ideal combination of doses is achieved. This arbitrary process can cost a few hours or a day to finish. My research aims to automate the generation of the plans by implementing an optimization algorithm on top of the treatment planning system using gradient …

Husky Masquerade, Amila D. Desilva, Bryant A. Julstrom

Huskies Showcase

Award for "Best Demonstration".

Abstract

Face detection is the process where machines identify faces within an image or visual field. Face detection is used in analyzing emotions, and even in social networking applications, such as Snapchat. The underlying mechanism of face detection is to locate key landmarks on a person’s face. The goal is to detect faces using a webcam, find the facial landmarks of the detected faces, and overlay customized images relative to the facial landmarks. Machines need to be taught to detect faces. It is crucial to teach the program to identify the different types of jawlines. The …

Topical Analysis Of The Enron Emails Using Graph Theory, Casey Kalinowski

Student Scholar Showcase

The Enron Scandal of the early 2000s shook the financial world. The subsequent investigation of the Enron Corporation resulted in the arrests of many top-level executives, but are these employees the only ones responsible for the wide scale fraud in the company? A topical analysis of a social network of over 150 employees of the Enron Corporation using Graph Theory could result in new findings or prove that the investigators were correct in their original findings. The research is a retrospective analysis of a corpus of over 500,000 emails from more than 150 employees and top-level executives of the Enron …

Using Computer Algorithms To Elucidate Zebra Finch Reproductive Behaviour, Tanya T. Shoot, Sophie C. Edwards, Robert J. Martin, Susan D. Healy, David F. Sherry, Mark J. Daley

Western Research Forum

Birds that experience variation in climatic conditions must maintain a stable nest temperature during incubation for successful hatching of offspring. Varying nest structure and incubation behaviour may be the methods birds use to regulate nest temperature. We used a modeling approach to investigate how birds adjust incubation behaviour to ambient temperature.

Hidden Markov Models (HMM) have been used previously to predict the spatial distribution of animals based on the models’ ability to classify movement behaviour. We used a HMM to predict zebra finch (Taeniopygia guttata) incubation behaviour and nest structure from a nest temperature data set. The full …

Towards Robust Classification In Adversarial Learning Using Bayesian Games, Anna Buhman

UNO Student Research and Creative Activity Fair

A well-trained neural network is very accurate when classifying data into different categories. However, a malicious adversary can fool a neural network through tiny changes to the data, called perturbations, that would not even be detectable to a human. This makes neural networks vulnerable to influence by an attacker. Generative Adversarial Networks (GANs) have been developed as one possible solution to this problem [1]. A GAN consists of two neural networks, a generator and a discriminator. The discriminator tries to learn how to classify data into categories. The generator stands in for the attacker and tries to discover the best …

Extension Of The Ezsmt Software System For Non-Tight Constraint Answer Set Programs, Da Shen

UNO Student Research and Creative Activity Fair

Answer set programming (ASP) is a programming language that plays a critical role in the development of software applications in areas of science, humanities, and industry. Yet, it is faced with some challenges. Therefore, researchers develop a related paradigm called constraint answer set programming (CASP) to tackle several issues of ASP tools. Recently, a method is proposed to find solutions to CASP programs by using satisfiability modulo theories (SMT) solvers. SMT solvers are high-performance systems stemming from the software verification community.

This SMT-based approach is implemented in a system called EZSMT, which often outperforms its peers. Yet, it has several …

Intelligent And Human-Aware Decision Making For Semi-Autonomous Human Rehabilitation Assistance Using Modular Robots, Anoop Mishra

UNO Student Research and Creative Activity Fair

Modular Self-reconfigurable Robots (MSRs) are robots that can adapt their shape and mobility while performing their operations. We are developing an MSR called MARIO (Modular Robots for Assistance in Robust and Intelligent Operations) to assist patients with spinal cord injury in performing daily living tasks. In this research, we are investigating computational techniques that will enable MARIO to autonomously adapt its shape while performing an assistive task, and, while remaining aware of the human user’s satisfaction in receiving assistance from MARIO. We are developing semi-autonomous decision making techniques within a computational framework called shared autonomy that will adapt MARIO’s movements …